BACKGROUND Indiscriminate use of broad‐spectrum insecticides can have deleterious effects on insects and the environment. The use of nanoparticles synthesized from microbes has recently gained importance as a safe alternative to conventional insecticides. Recently, zinc oxide (ZnO) nanoparticles synthesized using Bacillus thuringiensis have shown insecticidal potential; however, in addition to its acute toxicity, it is necessary to determine possible sublethal effects at the organismal level to understand the toxicity of a new insecticide. Bt‐derived enzymes such as nitrate reductase and other biomolecules play a vital role in the reduction of metal ions to metal nanoparticles. Here, we assessed the acute toxicity and sublethal effects of ZnO nanoparticles produced in the culture supernatant of B. thuringiensis ser. israelensis (Bti) as a reducing agent on the biological traits of Musca domestica. RESULTS Concentration–response larval bioassays using different concentrations of ZnO‐Bti‐supernatant nanoparticles revealed LC10, LC20, LC50 and LC90 values of 4.17, 6.11, 12.73 and 38.90 μg g−1 of larval diet, respectively. Exposure of M. domestica larvae to two concentrations (LC10 and LC20) resulted in a lengthened developmental time (egg to adult) and preoviposition period, and reduced fecundity, survival, longevity and oviposition period. Furthermore, population parameters including net reproductive rate, mean generation time, age‐specific survival rate, fecundity, life expectancy and reproductive values, analyzed following age‐stage and two‐sex life table theory, were significantly decreased after exposure to these concentrations of ZnO‐Bti‐supernatant nanoparticles compared with the control. CONCLUSION ZnO‐Bti‐supernatant nanoparticles were shown to be toxic to M. domestica. Exposure of M. domestica to low concentrations of ZnO‐Bti‐supernatant nanoparticles resulted in negative transgenerational effects on progeny production in this fly. © 2022 Society of Chemical Industry.

Background The current study was conducted to find out an eco-friendly and cost-efficient way to prepare copper nanoparticles (CuNPs) by utilizing Grewia asiatica L. leaf extract, which was found to be a very effective antimicrobial and larvicidal chemical. Methods Characterization of nanoparticles was also carried out by utilizing Fourier transform infrared spectroscopy (FTIR) analysis, scanning electron microscope along with X-ray diffraction analysis (XRD). The artificially prepared nanoparticles in the laboratory were approximately in the range of 2 µm in size and crystalline in nature. The CuNPs were tested for their antimicrobial activity against different types of fungi and bacteria, also some mosquito and termite. All the results and observations were tested with a one-way analysis of variance keeping the probability level at < 0.0001. Results The copper nanoparticles exhibit significant antibacterial and antifungal activities and are also found lethal for many mosquito and termite species. Antibacterial activity was checked against Escherichia coli and Bacillus subtilis, their zone of inhibition was 17 nm and 20 nm, respectively. The antifungal potential was checked against Aspergillus niger and Aspergillus oryzae and the zone of inhibition was recorded at 20 mm and 23 mm, respectively. CuNPs were also found lethal for many mosquitoes and maximum efficacy of CuNPs against Aedes aegypti larva was observed at 100 mg/ml after 24 h. Termite species such as Heterotermes indicola were exposed to CuNPs and the highest mortality rate in termites was seen at 100 ppm concentration of CuNPs. Current research provides the first investigation of CuNPs of G. asiatica as a larvicidal and as an anti-termite. G. asiatica garden-fresh leaves were collected from Railway colony Mughalpura. Conclusions This study proves that CuNPs have a toxic effect on insects and can also be utilized as a biological control of insects. By using such a scientific approach, the scientists can lower the costs of chemical usage and a biodegradable alternative could be provided.

The molecular hybrid approach is very significant to combat various drug-resistant disorders. A simple, convenient, and cost-effective synthesis of thiazole-based chalcones is accomplished, using a molecular hybrid approach, in two steps. The compound 1-(2-phenylthiazol-4-yl)ethanone (3) was used as the main intermediate for the synthesis of 3-(arylidene)-1-(2-phenylthiazol-4-yl)prop-2-en-1-ones (4a-f). Thin layer chromatography was used to testify the formation and purity of all synthesized compounds. Further structural confirmation of all compounds was achieved via different spectroscopic techniques (UV, FT-IR, 1 H-and 13 C-NMR) and elemental analysis. All synthesized compounds were tested for their α-amylase inhibition and antioxidant potential. The cytotoxic property of compounds was also tested with in vitro haemolytic assay. All tested compounds showed moderate to excellent α-amylase inhibition and antioxidant activity. All tested compounds are found safe to use due to their less toxicity when compared to the standard Triton X. The molecular docking simulation study of all synthesized compounds was also conducted to examine the best binding interactions with human pancreatic αamylase (pdb: 4 W93) using AutodockVina. The molecular docking results authenticated the in vitro amylase inhibition results, i.e., 3-(3-Methoxyphenyl)-1-(2-phenylthiazol-4-yl)prop-2-en-1-one (4e) exhibited lowest IC 50 value 54.09 � 0.11 μM with a binding energy of À 7.898 kcal/mol.

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In autonomous systems, self-awareness capabilities are useful to allow artificial agents to detect abnormal situations based on previous experiences. This paper presents a method that facilitates the incremental learning of new models by an agent. Available learned models can dynamically generate probabilistic predictions as well as evaluate their mismatch from current observations. Observed mismatches are grouped through an unsupervised learning strategy into different classes, each of them corresponding to a dynamic model in a given region of the state space. Such clusters define switching Dynamic Bayesian Networks (DBNs) employed for predicting future instances and detect anomalies. Inferences generated by several DBNs that use different sensorial data are compared quantitatively. For testing the proposed approach, it is considered the multisensorial data generated by a robot performing various tasks in a controlled environment and a real autonomous vehicle moving at a University Campus.